Lmst

#LowDimensional

🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌

🌍 https://arxiv.org/abs/2508.15929

#CompNeuro #MachineLearning #DataVisualization

Figure 4: 2D embeddings of 23 800 cells from the mouse cortex (Tasic et al., 2018). Colors correspond to transcriptomic cell types,
taken from the original publication. The first two principal components explained 49.1% of the variance of the preprocessed data.
As Laplacian eigenmaps had many almost-overlapping points, they are shown with larger semi-transparent markers.

Figure 6: 2D embeddings of 3 450 human genotypes from 26 global populations (The 1000 Genomes Project Consortium, 2015).
Colors represent the sampling population. The first two principal components together explained 5.8% of the total variance. For
population abbreviations used to annotate the UMAP embedding, see the original publication. As Laplacian eigenmaps had many
almost-overlapping points, they are shown with large semi-transparent markers.

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